| // Ceres Solver - A fast non-linear least squares minimizer | |
| // Copyright 2023 Google Inc. All rights reserved. | |
| // http://ceres-solver.org/ | |
| // | |
| // Redistribution and use in source and binary forms, with or without | |
| // modification, are permitted provided that the following conditions are met: | |
| // | |
| // * Redistributions of source code must retain the above copyright notice, | |
| // this list of conditions and the following disclaimer. | |
| // * Redistributions in binary form must reproduce the above copyright notice, | |
| // this list of conditions and the following disclaimer in the documentation | |
| // and/or other materials provided with the distribution. | |
| // * Neither the name of Google Inc. nor the names of its contributors may be | |
| // used to endorse or promote products derived from this software without | |
| // specific prior written permission. | |
| // | |
| // THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" | |
| // AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE | |
| // IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE | |
| // ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE | |
| // LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR | |
| // CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF | |
| // SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS | |
| // INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN | |
| // CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) | |
| // ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE | |
| // POSSIBILITY OF SUCH DAMAGE. | |
| // | |
| // Author: sameeragarwal@google.com (Sameer Agarwal) | |
| #ifdef CERES_HAS_GTEST | |
| #ifndef CERES_INTERNAL_NUMERIC_DIFF_TEST_UTILS_H_ | |
| #define CERES_INTERNAL_NUMERIC_DIFF_TEST_UTILS_H_ | |
| #include <random> | |
| #include "ceres/cost_function.h" | |
| #include "ceres/internal/export.h" | |
| #include "ceres/sized_cost_function.h" | |
| #include "ceres/types.h" | |
| namespace ceres::internal { | |
| // Noise factor for randomized cost function. | |
| static constexpr double kNoiseFactor = 0.01; | |
| // Default random seed for randomized cost function. | |
| static constexpr unsigned int kRandomSeed = 1234; | |
| // y1 = x1'x2 -> dy1/dx1 = x2, dy1/dx2 = x1 | |
| // y2 = (x1'x2)^2 -> dy2/dx1 = 2 * x2 * (x1'x2), dy2/dx2 = 2 * x1 * (x1'x2) | |
| // y3 = x2'x2 -> dy3/dx1 = 0, dy3/dx2 = 2 * x2 | |
| class CERES_NO_EXPORT EasyFunctor { | |
| public: | |
| bool operator()(const double* x1, const double* x2, double* residuals) const; | |
| void ExpectCostFunctionEvaluationIsNearlyCorrect( | |
| const CostFunction& cost_function, NumericDiffMethodType method) const; | |
| }; | |
| class EasyCostFunction : public SizedCostFunction<3, 5, 5> { | |
| public: | |
| bool Evaluate(double const* const* parameters, | |
| double* residuals, | |
| double** /* not used */) const final { | |
| return functor_(parameters[0], parameters[1], residuals); | |
| } | |
| private: | |
| EasyFunctor functor_; | |
| }; | |
| // y1 = sin(x1'x2) | |
| // y2 = exp(-x1'x2 / 10) | |
| // | |
| // dy1/dx1 = x2 * cos(x1'x2), dy1/dx2 = x1 * cos(x1'x2) | |
| // dy2/dx1 = -x2 * exp(-x1'x2 / 10) / 10, dy2/dx2 = -x2 * exp(-x1'x2 / 10) / 10 | |
| class CERES_NO_EXPORT TranscendentalFunctor { | |
| public: | |
| bool operator()(const double* x1, const double* x2, double* residuals) const; | |
| void ExpectCostFunctionEvaluationIsNearlyCorrect( | |
| const CostFunction& cost_function, NumericDiffMethodType method) const; | |
| }; | |
| class CERES_NO_EXPORT TranscendentalCostFunction | |
| : public SizedCostFunction<2, 5, 5> { | |
| public: | |
| bool Evaluate(double const* const* parameters, | |
| double* residuals, | |
| double** /* not used */) const final { | |
| return functor_(parameters[0], parameters[1], residuals); | |
| } | |
| private: | |
| TranscendentalFunctor functor_; | |
| }; | |
| // y = exp(x), dy/dx = exp(x) | |
| class CERES_NO_EXPORT ExponentialFunctor { | |
| public: | |
| bool operator()(const double* x1, double* residuals) const; | |
| void ExpectCostFunctionEvaluationIsNearlyCorrect( | |
| const CostFunction& cost_function) const; | |
| }; | |
| class ExponentialCostFunction : public SizedCostFunction<1, 1> { | |
| public: | |
| bool Evaluate(double const* const* parameters, | |
| double* residuals, | |
| double** /* not used */) const final { | |
| return functor_(parameters[0], residuals); | |
| } | |
| private: | |
| ExponentialFunctor functor_; | |
| }; | |
| // Test adaptive numeric differentiation by synthetically adding random noise | |
| // to a functor. | |
| // y = x^2 + [random noise], dy/dx ~ 2x | |
| class CERES_NO_EXPORT RandomizedFunctor { | |
| public: | |
| RandomizedFunctor(double noise_factor, std::mt19937& prng) | |
| : noise_factor_(noise_factor), | |
| prng_(&prng), | |
| uniform_distribution_{-noise_factor, noise_factor} {} | |
| bool operator()(const double* x1, double* residuals) const; | |
| void ExpectCostFunctionEvaluationIsNearlyCorrect( | |
| const CostFunction& cost_function) const; | |
| private: | |
| double noise_factor_; | |
| // Store the generator as a pointer to be able to modify the instance the | |
| // pointer is pointing to. | |
| std::mt19937* prng_; | |
| mutable std::uniform_real_distribution<> uniform_distribution_; | |
| }; | |
| class CERES_NO_EXPORT RandomizedCostFunction : public SizedCostFunction<1, 1> { | |
| public: | |
| RandomizedCostFunction(double noise_factor, std::mt19937& prng) | |
| : functor_(noise_factor, prng) {} | |
| bool Evaluate(double const* const* parameters, | |
| double* residuals, | |
| double** /* not used */) const final { | |
| return functor_(parameters[0], residuals); | |
| } | |
| private: | |
| RandomizedFunctor functor_; | |
| }; | |
| } // namespace ceres::internal | |
| #endif // CERES_INTERNAL_NUMERIC_DIFF_TEST_UTILS_H_ | |
| #endif // CERES_HAS_GTEST |